Systems that utilize loss-less compression techniques generally do not perform well on image data, such as photographs and other high-resolution graphics. While achieving 100:1 compression on data associated with text and business graphics (line art, bar charts, and the like), complex loss-less compression algorithms usually achieve less than a 2:1 compression of image data. Conversely, while data associated with photographs and other images can be compressed effectively with a “lossy” algorithm without significantly affecting perceptible image quality, lossy algorithms visibly degrade text data (such as by leaving visual artifacts), even at relatively low levels of compression. Moreover, lossy compression techniques do not achieve compression ratios as high as those generally expected where data is based on text images. Still further, the advantages of JPEG-like compression over other techniques are reduced when compressing image data that have been scaled using a pixel-replication scaling algorithm common to rasterized compound documents (e.g., 150 dot-per-inch (“dpi”) image data scaled up to a resolution of 300-dpi or 600-dpi). Thus, while lossy algorithms perform well on image data, loss-less algorithms provide better results on text-based data.
Solutions that use a mix of lossy and loss-less data compression are often slow and complex. For example, text and image data are sometimes separated to different channels, one containing the images using a lossy compression technique, like JPEG, and the other using a loss-less compression technique better-suited for text and simple business graphics. This separation of data into individual channels can be slow and the results are dependent on the architecture of the rasterization engine that initially rasterized the compound document. Moreover, the use of a lossy algorithm reduces data processing speeds, which slows the performance of devices configured to output “hard copies” onto print media. Again, the advantages of a JPEG-type algorithm are reduced for images that have been scaled. Moreover, the relatively slow nature of JPEG is not improved even when compressing high resolution pixel replicated image data.
Thus, currently known compression and decompression techniques fail to provide satisfactory results in many applications.